Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning.

ABSTRACT: In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general-purpose inverse design approach is presented using generative inverse design networks. This ML-based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning st

SUBMITTER: Chen CT 

PROVIDER: S-EPMC7055566 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature